The request names Pinecone, so Pinecone comes first: a managed vector database for AI knowledge, reachable via an MCP server, an SDK and a skill 111. Three alternatives that also store and search vectors are Qdrant 212, Weaviate 919 and turbopuffer 6.
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Pineconepinecone.ioverified and signed
Managed vector database for AI knowledge that stores and queries vector indexes, offered through an MCP server, an SDK and a skill 111.
official:MCP server · @pinecone-database/mcpSDK · @pinecone-database/pineconeskill · pinecone+10 community tools built for it
for agents: pinecone · vector · database
- 2
Qdrantqdrant.techverified and signed
Open-source vector search engine with an MCP server, a client SDK and an add-points and search-points action for vector similarity search 212.
official:MCP server · mcp-server-qdrantSDK · qdrant-clientskill · qdrant-clients-sdkSDK · @qdrant/js-client-restskill · qdrant-search-quality+45 community tools built for it
for agents: search_points · vector · add_points_to_collection
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Weaviateweaviate.ioverified and signed
AI database for vector search with an MCP server and a Python client SDK 919.
official:MCP server · MCP Server WeaviateSDK · weaviate-clientCLI · weaviaterepository · irpapersSDK · weaviate-client+5 community tools built for it
for agents: weaviate
- 4
turbopufferturbopuffer.comwebsite read, tools not yet checked
Vector and full-text search engine built on object storage, offered as an API 6.
Sources
- 1The trusted AI knowledge company | Pineconepinecone.io · read 2026-10-29
- 2Qdrant - Vector Search Engineqdrant.tech · read 2026-10-29
- 6turbopuffer - fast search engine built on object storageturbopuffer.com · read 2026-10-29
- 9The AI database developers love | Weaviateweaviate.io · read 2026-10-29
- 11pinecone: declares "pinecone" (the record we read and signed)unfragile.ai · read 2026-10-24
- 12Qdrant (Activepieces): declares "search_points" (the record we read and signed)unfragile.ai · read 2026-10-24
- 19weaviate-client: declares "weaviate" (the record we read and signed)unfragile.ai · read 2026-10-24
For agents: the 10 actions behind this answer
pinecone · pinecone verified and signed
Pinecone vector database integration. Streamline your Pinecone development with powerful tools for managing vector indexes, querying data, and rapid prototyping. Use slash commands like /quickstart to generate AGENTS.md files and initialize Python projects and /query to quickly explore indexes. Acce
search_points · Qdrant (Activepieces) verified and signed
Search Points — Search for points closest to your given vector (= embedding)
list_tables · Supabase (Activepieces) verified and signed
List Tables — Returns a list of all public tables and views in the database
set_vector_in_hash · Redis MCP Server found in code, not yet checked
Store a vector as a field in a Redis hash.
vector-database · lance-namespace verified and signed
vector · fastembed verified and signed
· Supabase Realtime website read, tools not yet checked
Build production-grade applications with a Postgres database, Authentication, instant APIs, Realtime, Functions, Storage and Vector embeddings. Start for free.
· turbopuffer website read, tools not yet checked
vector and full-text search built on object storage: fast, 10x cheaper, and extremely scalable
AgentFront · AgentFront verified and signed
Use when building MCP (Model Context Protocol) servers with
kibana-vega · kibana-vega verified and signed
Create Vega and Vega-Lite visualizations with ES|QL data sources in Kibana. Use when building custom charts, dashboards, or programmatic panel layouts beyond standard Lens charts.
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.